A Geometric Approach for Regularization of the Data Term in Stereo-Vision
Journal of Mathematical Imaging and Vision
Local stereo matching with adaptive support-weight, rank transform and disparity calibration
Pattern Recognition Letters
Stereo vision for robotic applications in the presence of non-ideal lighting conditions
Image and Vision Computing
Stereo vision enabling precise border localization within a scanline optimization framework
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Adaptive rank transform for stereo matching
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Stereo matching using weighted dynamic programming on a single-direction four-connected tree
Computer Vision and Image Understanding
Analytical dynamic programming matching
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Robust 3D human face reconstruction by consumer binocular-stereo cameras
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Really quick shift: image segmentation on a GPU
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Fast and Accurate Stereo Vision System on FPGA
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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In this paper, we present a novel stereo algorithm that combines the strengths of region-based stereo and dynamic programming on a tree approaches. Instead of formulating an image as individual scan-lines or as a pixel tree, a new region tree structure, which is built as a minimum spanning tree on the adjacency-graph of an over-segmented image, is used for the global dynamic programming optimization. The resulting disparity maps do not contain any streaking problem as is common in scanline-based algorithms because of the tree structure. The performance evaluation using the Middlebury benchmark datasets shows that the performance of our algorithm is comparable in accuracy and efficiency with top ranking algorithms.